3 Practical Reasons Retailers Should Adopt AI in 2019

ai+man_blog

In the world of retail, the goal is simple: sell product. 

However, reaching that goal is a completely different story. When you throw competition, customer expectations, stores, inventory, logistics, and fulfillment into the mix, things reach a whole new level of complexity. The challenge is really two-fold:

  1. ) First, demand is seemingly unpredictable, with a multitude of factors influencing a consumer’s desire to want to buy something. 
  2. ) Secondly, there’s the issue of optimization – how can retailers leverage their inventory more productively? 

Overcoming these challenges requires retailers to better predict demand and optimize their inventory decisions more effectively to ultimately maximize every opportunity for a sale. Artificial intelligence and advanced analytics technologies are growing to be the tools of choice for retailers when it comes to optimizing inventories as customer expectations force retailers to invest heavily in digital capabilities.

Many retailers, according to Gartner, are “simply not optimizing their most significant assets.” What asset is more important than a retailer’s inventory? Here are three reasons retailers should adopt AI for inventory optimization in 2019:

1.) Lack of Localization Leads to Missed Opportunities 

“In retail, there is only one battle that matters — the fight for the customer.” — Thomas O’Connor, Senior Director, Global Supply Chain Research and Advisory at Gartner 

The fight for the customer is a never-ending battle for every shopper’s attention. Whether it’s piquing a passerby’s interest to step foot into your store or enticing online browsers with a reason to keep scrolling, every retailer knows the value of capturing these fleeting opportunities. 

Today, localization is no longer defined by geographic region, but rather by wherever customer demand can be met. This means having the right product at the right place when a shopper gets the slightest inclination to make a purchase.

According to Gartner, retailers must “get even closer to their customers using new digital capabilities such as advanced analytics and AI to segment customers by behaviors and validate their evolving wants and needs.” It’s pairing a retailer’s data with the predictive capabilities of such technology to forecast demand at a truly localized level, all in an effort to understand where to put exactly what the customer wants when they want it. 

2.) Excellent Order Fulfillment is Expected 

While placing the right product in the most opportune spot is one piece of the puzzle, the delivery experience is another. We already know one thing: customers expect a seamless and effortless delivery experience – it’s a given nowadays. Quick and cheap fulfillment is what’s at stake, but retailers who monopolize the data and analytics available will prevail in the delivery arena:

“Many believe the winner will be ultimately decided by the supply chain analytics competency associated with Omni channel retailing. Whoever can convert massive volumes of data into a valuable decision- making consumer tool will no doubt have the advantage. Speed and competency of transforming raw consumer information into supply chain solutions to satisfy customer preferences might decide the ultimate winner.”- Rutgers Business School 

As an example, retailers like Lucky Brand and ALDO Group are managing their store inventories more productively to help meet fulfillment expectations. By weaving in intelligence from advanced analytics tools into their ship-from-store fulfillment decisions, they’re ensuring the right inventory is being leveraged. This means predicting what store was going to sell out or which store to take inventory from in order to speed up the overall turn in-store and delivery speed to the customer. 

3.) The Goal? Not ‘Just Any’ Sale, a Full Price Sale 

As with any merchandising decision, the goal may be to sell a product – but the real feat is the ability to sell that product at full price:

“Retailers define success at the end of a season in many ways. Did we hit our retail sales or gross margin dollar plan? Did our biggest investments sell well? One of the most common metrics used is full-price sell through: the percentage of units that sold at full price. Full-price sell through varies by brand, garment and season […], but a good industry standard is around 60 percent for the intended life of a given product, meaning a retailer expects to sell the other 40 percent of units on markdown.” – The Business of Fashion

This percentage of product marked down represents an opportunity for improvement, especially in an industry where dead inventory — inventory bumped from markdown sections that exist in a warehouse or stockroom — is costing retailers up to $50 billion a year. 

Every merchandising decision planners and buyers make up front, during pre-season planning, impacts the ability to meet these sell-through goals. Additionally, in-season fulfillment and allocation decisions also contribute to the opportunities to sell product at full-price.

The reality is, predicting demand is hard. Plus, making the optimal decision with the inventory you already invested in is even harder at scale with millions of possibilities and scenarios to consider. With advanced analytics, retailers can hone their intuition with data in meaningful ways to shed light on customer preferences and future demand, fostering more calculated decisions with the assets merchants actually have control over. 

New Call-to-action

Topics: brick-and-mortar retail, merchandise planning, data, inventory, e-commerce, demand prediction, order fulfillment, advanced analytics, artificial intelligence, allocation, markdowns

Ready for more?

Request a Demo